1. Field of the Invention
The invention relates to a passive channel calibration method for a high frequency surface wave radar (HFSWR) by a non-linear antenna array.
2. Description of the Related Art
High frequency surface wave radar uses the characteristic of vertically-polarized high frequency electromagnetic wave whose attenuation is low when it transmits along the ocean surface. It has the capability of over-the-horizon detection of the targets below the line of sight, such as vessels, low-altitude planes, missiles and so on. In addition, HFSWR can extract the ocean state information (wind field, wave field, current field, etc.) from the first order and the second order radar ocean echoes. Then the real time monitoring of large-scope, high-precision and all-weather to the ocean can be achieved.
Influenced by multiple factors, such as difference in hardware, non-ideal characteristic of receiving channels and surrounding electromagnetic environment, amplitude-phase characteristics of each of the radar channels including antenna are different, which causes inconsistency between the amplitude and phase values of the same echo signal passing through different channels, which is referred to as channel mismatch. The channel mismatch may increase errors of, or even invalidate beam scan and direction-of-arrival estimation. The channel mismatch is a key point that affects detecting performance of HFSWR. Steps must be taken to restrict the channel mismatch within a given range to ensure operating efficiently of the radar: firstly, proper measurements should be taken (e.g. component selection) to ensure consistency of each channel during production thereof; secondly, the channel mismatch coefficients can be measured or estimated, and characteristic difference between channels is further reduced via calibration.
Existing channel calibration methods can be categorized into active ones and passive ones. In the active calibration method, the auxiliary signal source is located in an open area far away from the antenna array to send calibration signals, and the output of each receiving channel is measured; then, channel mismatch information can be obtained by deducting the phase difference caused by azimuth of known signal source and array space position. In the passive calibration method, no auxiliary signal source whose azimuth has been accurately obtained is needed, the channel mismatch coefficients are estimated directly via the received measuring data and some apriority information (e.g. array format), and compensative calibration is implemented. There are other passive calibration methods that can realize joint estimation of signal azimuth and channel mismatch. Detailed description of the passive calibration method can be found in the book “Spatial Spectrum Estimation and its Applications” (Press of China University of Science and Technology, 1997) by Liu D S and Luo J Q.
Influenced by many factors, such as landform condition, operating wavelength, electromagnetic wave propagation, radar system, antenna array, (solid) target echoes, ocean echoes, noise interference, it is difficult to implement channel calibration for HFSWRs. The prior art can only partly solve the problems, and is time-consuming and expensive. Sea surface is in front of the radar antenna array, so if the active calibration is adopted, the auxiliary signal source can only be located on a ship or an island, which will be troublesome and expensive to maintain and difficult to work steadily. Existing passive calibration methods need complicated iterative computing, which means a heavy computing load and may not meet a real-time requirement, and even have a possibility of converging to the local optimum, not the global one, which may result in completely inaccurate estimated values. Applicable conditions of the existing passive calibration method cannot be satisfied due to the difference between an actual radar system and an ideal model. The channel calibration has become a big technical problem that restricts detecting performance of HFSWR and affects actual application thereof, and must be solved properly.
The Radio Wave Propagation Lab of Wuhan University has considered using the reflection signal of a known natural or artificial object on the ocean as a calibration signal. The calibration signal can be detected from echoes if the range and the velocity of a reflection source are already known, and mismatch coefficients of each channel are estimated based on the azimuth of the reflection source. Detailed description can be found in Chinese Patent Application No. 03128238.5 entitled “A method for array channel calibration using ocean echoes”. The invention may utilize echoes from fixed reflection objects such as islands, lighthouses, drilling platforms and so on, which overcome problems such as displacement and maintenance of auxiliary signal sources and extra hardware cost, and online real time automatic calibration may be implemented. However, the invention is not suitable to a sea area without fixed reflection objects, and is affected by disadvantageous factors such as noise interference, ship echoes, multi-path effect and so on. As that invention proposes a technology for separating and detecting the ocean echoes with single azimuths, whose frequency spectrums are non-overlapped, which meets basic requirements of the passive channel calibration method of this invention, and will be described in further detail below.
HFSWR commonly adopts a frequency modulated interrupted continuous wave (FMICW) waveform, which has been explained in detail in the paper “Target Detection and Tracking With a High Frequency Surface wave Radar” (Rafaat Khan, et al., IEEE Journal of Oceanic Engineering, 1994, 19(4): 540-548). By the waveform, a range-Doppler (velocity) two-dimension echo spectrum can be obtained by mixing, low-pass filtering, A/D converting and two dimension FFT (shown as FIG. 1) after the ocean echoes (including those from ocean surface waves and from solid targets) enter a receiver (as shown in FIG. 2). In the two-dimension echo spectrum, the ocean echoes are separated according to the range and the velocity, and distributed on many spectrum points. If coherence accumulation time of the second FFT (Doppler transformation) is rather long (about 10 minutes), the radar may obtain very high velocity resolution, which means the number of the spectrum points in the two-dimension echo spectrum corresponding to the ocean echoes may be above 1000, and it is suitable to detect the single-azimuth echoes whose frequency spectrums are non-overlapped via a statistical method.
Detection of the single-azimuth echoes is implemented by statistical analysis of a two-dimension echo spectrum of an array in a given form (as shown in FIG. 3). The array in a particular form is composed of array elements 1-4, whose position coordinates thereof is (xi, yi), and the output of a corresponding two-dimension echo spectrum point is Yi, i=1, 2, 3, 4. An array-element couple A1 is composed of elements 1 and 2, and the other array-element couple A2 is composed of elements 3 and 4. There is translation invariance between A1 and A2, then
  {                                          (                                          x                2                            ,                              y                2                                      )                    =                      (                                                            x                  1                                +                d                            ,                              y                1                                      )                                                                    (                                          x                4                            ,                              y                4                                      )                    =                      (                                                            x                  3                                +                d                            ,                              y                3                                      )                                 
      Defining    ⁢                  ⁢          η      1        =                              Y          2                ⁢                  Y          3                                      Y          1                ⁢                  Y          4                      .  It is easy to prove that, in an ideal condition without noise, η1 corresponding to a single-azimuth spectrum point in the two-dimension echo spectrum is an invariable parameter, which only relates to channel mismatch and is marked as η′1. In an actual system, the noise is inevitable, so η1 corresponding to the single-azimuth spectrum points are centrally distributed around η1. On the other hand, it can be known from simple analysis and numerical modeling that η1 corresponding to a multi-azimuth spectrum point is a variable relating to target parameters of ranges, (radial) velocities, azimuths and echo signal amplitudes, whose randomicity result in a randomly distributed state of η1. To summarize, if η1 corresponding to spectrum points that exceed a signal-noise-ratio threshold in the two-dimension echo spectrum are marked on the complex plane, it will be found that highly-aggregative phenomena appears in only one region (around η′1), where most of η1 are corresponding to the single-azimuth spectrum points.
            Defining      ⁢                          ⁢              η        2              =                            Y          2                ⁢                  Y          1          *                                      Y          4                ⁢                  Y          3          *                      ,from analysis similar to the above, an aggregative region, where most of η2 are corresponding to the single-azimuth spectrum points, may also appear on the complex plane.
            Defining      ⁢                                        ⁢                                      ⁢              η        3              =                            Y          2                ⁢                  Y          4          *                                      Y          1                ⁢                  Y          3          *                      ,an aggregative region, where most of η3 are corresponding to the single-azimuth spectrum points, may appear on the complex plane in the same way.
Discovered through theoretic analysis and numerical modeling, the probability that η1, η2 and η3 corresponding to a multi-azimuth spectrum point simultaneously drop into their respective aggregative regions is very small, and therefore whether η1, η2 and η3 simultaneously drop into their respective aggregative regions can be used as a criterion to detect single-azimuth spectrum points. As a combination of translation invariant dual array-element couples, A1 and A2 compose an array in a given form for detecting single-azimuth echoes (spectrum points). If there is more than one combination of translation invariant dual array-element couples in the array, the single-azimuth spectrum points can be filtered out by a criterion whether a spectrum point is detected by multiple combinations of translation invariant dual array-element couples. An array containing at least a combination of translation invariant dual array-element couples, such as a uniform linear array (or a uniform plane array), is very common.